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Automation applications are pushing the deployment of many high DoFmanipulators in warehouse and manufacturing environments. This has motivatedmany efforts on optimizing manipulation tasks involving a single arm.Coordinating multiple arms for manipulation, however, introduces additionalcomputational challenges arising from the increased DoFs, as well as thecombinatorial increase in the available operations that many manipulators canperform, including handoffs between arms. The focus here is on the case ofpick-and-place tasks, which require a sequence of handoffs to be executed, soas to achieve computational efficiency, asymptotic optimality and practicalanytime performance. The paper leverages recent advances in multi-robot motionplanning for high DoF systems to propose a novel multi-modal extension of thedRRT* algorithm. The key insight is that, instead of naively solving a sequenceof motion planning problems, it is computationally advantageous to directlyexplore the composite space of the integrated multi-arm task and motionplanning problem, given input sets of possible pick and handoff configurations.Asymptotic optimality guarantees are possible by sampling additional picks andhandoffs over time. The evaluation shows that the approach finds initialsolutions fast and improves their quality over time. It also succeeds infinding solutions to harder problem instances relative to alternatives and canscale effectively as the number of robots increases.

Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs